Causality in the Age of Machine Learning: A Philosophical Analysis of Causal Discovery by Artificial Intelligence

Recent advances in machine learning, particularly in data-driven and predictive modeling, have led to remarkable successes across a wide range of scientific and practical domains. At the same time, these successes have raised fundamental questions in the philosophy of science concerning the role of explanation and causality in a scientific practice increasingly centered on statistical prediction. In response to the limitations of purely correlation-based models a growing body of work under the heading of Causal Artificial Intelligence has sought to reintroduce causal structure into machine learning.

This paper offers a philosophical analysis of the notion of causality employed in contemporary machine learning, with particular attention to causal discovery and intervention-based frameworks. It argues that the causal relations represented or inferred by machine learning models should be understood neither as direct representations of the world’s fundamental causal mechanisms nor as merely instrumental devices for improving predictive performance. Instead, the paper proposes an alternative interpretation according to which causality in machine learning constitutes an intervention-dependent, mid-level explanatory structure.

On this account, the epistemic legitimacy of causal models derives not from strong metaphysical commitments, but from their ability to remain invariant under interventions, support counterfactual reasoning, and enable out-of-distribution generalization. By situating causal modeling between predictive success and metaphysical realism, the paper shows how causal concepts can play an indispensable explanatory and decision-theoretic role in data-driven science without exceeding the epistemic limits of machine learning. This interpretation provides a coherent philosophical framework for understanding the renewed significance of causality in contemporary artificial intelligence.